[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DCG8XGEWDXG4C27ZZ1FEE4":3,"skill-same:01M6DCG8XGEWDXG4C27ZZ1FEE4":62},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":14,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M6DCG8XGEWDXG4C27ZZ1FEE4","cost-aware-llm-pipeline",null,"Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Fplatform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fcost-aware-llm-pipeline\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"cost aware llm pipeline","pipeline","cost","prompt","logic","caching","patterns","task","complexity","tracking","cost optimization","model","api","Budget","prompt caching","Optimization","routing","2026-11-10 10:12:30",false,"---\nname: cost-aware-llm-pipeline\ndescription: Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.\norigin: ECC\n---\n\n# Cost-Aware LLM Pipeline\n\nPatterns for controlling LLM API costs while maintaining quality. Combines model routing, budget tracking, retry logic, and prompt caching into a composable pipeline.\n\n## When to Activate\n\n- Building applications that call LLM APIs (Claude, GPT, etc.)\n- Processing batches of items with varying complexity\n- Need to stay within a budget for API spend\n- Optimizing cost without sacrificing quality on complex tasks\n\n## Core Concepts\n\n### 1. Model Routing by Task Complexity\n\nAutomatically select cheaper models for simple tasks, reserving expensive models for complex ones.\n\n```python\nMODEL_SONNET = \"claude-sonnet-4-6\"\nMODEL_HAIKU = \"claude-haiku-4-5-20251001\"\n\n_SONNET_TEXT_THRESHOLD = 10_000  # chars\n_SONNET_ITEM_THRESHOLD = 30     # items\n\ndef select_model(\n    text_length: int,\n    item_count: int,\n    force_model: str | None = None,\n) -> str:\n    \"\"\"Select model based on task complexity.\"\"\"\n    if force_model is not None:\n        return force_model\n    if text_length >= _SONNET_TEXT_THRESHOLD or item_count >= _SONNET_ITEM_THRESHOLD:\n        return MODEL_SONNET  # Complex task\n    return MODEL_HAIKU  # Simple task (3-4x cheaper)\n```\n\n### 2. Immutable Cost Tracking\n\nTrack cumulative spend with frozen dataclasses. Each API call returns a new tracker — never mutates state.\n\n```python\nfrom dataclasses import dataclass\n\n@dataclass(frozen=True, slots=True)\nclass CostRecord:\n    model: str\n    input_tokens: int\n    output_tokens: int\n    cost_usd: float\n\n@dataclass(frozen=True, slots=True)\n\n### 3. Narrow Retry Logic\n### 4. Prompt Caching\n### Composition\n### Pricing Reference (2025-2026)\n### Best Practices\n### Anti-Patterns to Avoid\n### When to Use\n","# Cost-Aware LLM Pipeline\n\nPatterns for controlling LLM API costs while maintaining quality. Combines model routing, budget tracking, retry logic, and prompt caching into a composable pipeline.\n\n## When to Activate\n\n- Building applications that call LLM APIs (Claude, GPT, etc.)\n- Processing batches of items with varying complexity\n- Need to stay within a budget for API spend\n- Optimizing cost without sacrificing quality on complex tasks\n\n## Core Concepts\n\n### 1. Model Routing by Task Complexity\n\nAutomatically select cheaper models for simple tasks, reserving expensive models for complex ones.\n\n```python\nMODEL_SONNET = \"claude-sonnet-4-6\"\nMODEL_HAIKU = \"claude-haiku-4-5-20251001\"\n\n_SONNET_TEXT_THRESHOLD = 10_000  # chars\n_SONNET_ITEM_THRESHOLD = 30     # items\n\ndef select_model(\n    text_length: int,\n    item_count: int,\n    force_model: str | None = None,\n) -> str:\n    \"\"\"Select model based on task complexity.\"\"\"\n    if force_model is not None:\n        return force_model\n    if text_length >= _SONNET_TEXT_THRESHOLD or item_count >= _SONNET_ITEM_THRESHOLD:\n        return MODEL_SONNET  # Complex task\n    return MODEL_HAIKU  # Simple task (3-4x cheaper)\n```\n\n### 2. Immutable Cost Tracking\n\nTrack cumulative spend with frozen dataclasses. Each API call returns a new tracker — never mutates state.\n\n```python\nfrom dataclasses import dataclass\n\n@dataclass(frozen=True, slots=True)\nclass CostRecord:\n    model: str\n    input_tokens: int\n    output_tokens: int\n    cost_usd: float\n\n@dataclass(frozen=True, slots=True)\n\n### 3. Narrow Retry Logic\n### 4. Prompt Caching\n### Composition\n### Pricing Reference (2025-2026)\n### Best Practices\n### Anti-Patterns to Avoid\n### When to Use\n",{"name":5,"origin":41,"description":7},"ECC","main","a298c929b6cbd662e1fce54302ce520d5ceea1a5","platform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fcost-aware-llm-pipeline","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill cost-aware-llm-pipeline",{"tool":54,"command":55},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill cost-aware-llm-pipeline",{"tool":57,"command":58},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill cost-aware-llm-pipeline","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Fplatform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fcost-aware-llm-pipeline\u002FSKILL.md",true,[],[63,84,115,142,156,172],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":36},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[70,71,72,73,74,26,75,76,77,78,79,80,81,82,83],"clarify requirements","clarify","requirements","risk","options","prompts","goal","scanning","use","context","repo","injection","execution","file",{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":95,"publishedAt":36},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[96,97,98,99,100,101,102,103,104,105,106,107,108,109,78,110,111,112,113,114],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":127,"publishedAt":36},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[121,124],{"categoryNo":122,"categoryName":123,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":125,"categoryName":126,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000008","测试与质量",[128,129,130,131,132,133,134,135,136,137,97,138,139,140,141],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":36},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[144,149,150,151,152,153,154,155],"setup","report","project","installation","project setup","issues","state",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":163,"publishedAt":36},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[162],{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[158,164,26,165,166,167,78,168,169,170,171],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":173,"name":149,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":36},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[149,151,112,178,153,179],"files","interactive"]